To enable users of cellular phone networks and other wireless communication networks to communicate reliable over the network using a cell phone, smart phone, iPad®, notebook or other wireless communication device using network assets, such as by way of non-limiting example repeaters and/or cellular base stations, including either full size (i.e. “macro”) base stations and/or so-called “small cells”. Such assets have antennae positioned at locations selected to provide network coverage and at least adequate wireless signal strength and network coverage. In the absence of adequate signal strength in a particular area, wireless communications to or from that area may be hampered, unreliable or impossible. Placing antennae of network assets at judiciously selected locations can often eliminate such areas or at least reduce their size and/or improve the signal strength available in them. Since considerable expense is entailed in the acquisition, installation, operation and maintenance of such network assets, it is desirable to position such assets strategically so that an accurately predicted signal strength and coverage can be provided in regions within an area of interest.
Limitations on wireless signal strength, and thus network coverage, are imposed as a result of path loss. Path loss refers to the attenuation (i.e. reduction in power density) of an electromagnetic wave as it propagates along a signal path. A wireless signal undergoes path loss in part because the wave front of the signal expands in size as distance from the antenna of its transmission source increases. In addition to the distance from the transmitter antenna, a variety of physical phenomena may also contribute to path loss including for example reflection, refraction, diffraction and/or absorption of the wireless signal. Path loss is significantly influenced by factors such as topography and nature of the environment in the signal path (urban or rural, vegetation and foliage). The ability to estimate or predict path loss as accurately as possible is important in designing, planning, operating, maintaining, troubleshooting, repair, upgrading and/or reconfiguring of wireless communication systems in an economically efficient manner.
Propagation modeling software tools capable of analyzing path loss, signal strength, interference and/or coverage in a particular geographic region are commercially available and in the prior art. An example of such a software tool is the signal propagation modeling tool distributed under the trade name Atoll, such as Atoll 3.3, available from the Chicago, Ill. of Forsk Société à Responsabilité Limitée which is based in of Blagnac, France. Another example of such a tool is the RF network planning and optimization platform distributed by Infovista S.A. of Les Ulis, France under the trade name Mentum Planet. Particulars of such propagation modeling tools, and how to make and use them are well known in the prior art and need not be explained in here detail beyond noting that one of the data inputs necessary in order to use such propagation modeling software tools to carry out a signal quality analysis, such as a path loss analysis, signal strength analysis and/or network coverage analysis, and one which is crucial to the accuracy of such determinations, is input data characterizing what is generally referred to in the trade as “clutter”.
According to prior art methods of placing an antenna of a radio access network (RAN) asset, the location of the antenna is determined based on a coverage analysis, path loss analysis, signal strength analysis or other type of signal quality analysis in which the clutter data relied upon in carrying out the analysis have been typically been made up either entirely, or at least in major part, of a plurality of so-called “cutter classes” of a type representing generalized standard definitions of land uses such as those indicated on land use maps. Examples of such land use clutter classes may include for example “dense urban”, “urban”, “suburban”, and “rural”. Each particular land use clutter class is assigned a universal value for each given propagation parameter, such as attenuation per unit distance of signal travel, in that particular land use clutter class. Thus, according to a signal quality analysis carried out based on land use clutter classes, the inventor has recognized that a wireless signal would virtually always be predicted to undergo less attenuation per unit distance travelled through a suburban environment than through a dense urban environment and would likewise virtually always be predicted to undergo less attenuation per unit distance travelled through a rural environment than through a suburban environment.
By way of example, FIG. 1 and FIG. 14 show satellite images of the earth encompassing an example area of interest. FIG. 2 is a color-coded map showing the land use clutter classes of the same area of the earth as that shown in FIG. 1 and FIG. 3 is a legend identifying individual land use clutter classes designated according to the color scheme used in FIG. 2. Correspondingly, FIG. 15 is a non-color coded map showing the land use clutter classes of the same area of the earth as that shown in FIGS. 1 and 14 and FIG. 12 is a legend identifying individual land use clutter classes designated according to the non-color coding scheme used in FIG. 15. FIG. 4 is color-coded map illustrating the result of a signal quality analysis, in this example a coverage analysis, carried out for portions of a wireless communication network located within the same area as that represented in FIGS. 1 and 2 based on the land use clutter classes of FIG. 2 and FIG. 5 is a legend useful for interpreting the color coding scheme used in FIG. 4. Correspondingly, FIG. 17 is non-color coded map illustrating the result of a signal quality analysis, in this example a coverage analysis, carried out for portions of a wireless communication network located within the same area as that represented in FIGS. 14 and 15 based on the land use clutter classes of FIG. 15 and FIG. 18 is a legend useful for interpreting the non-color coding scheme used in FIG. 17.
As can be appreciated from FIGS. 4 and 5, the regions of highest predicted signal strength and thus, good performance, are those depicted in red. Regions predicted as having successively lower strengths, and thus successively lower performance, are depicted in FIG. 4 in dark orange, light orange, yellow, light green, dark green, light blue and dark blue, respectively. In the example illustrated by FIG. 4, regions depicted in red are generally centered within yellow regions correspond to the locations of the antennas of three cellular base stations which happen to exist in the example area of interest. In order to improve coverage by adding an additional radio access network (RAN) asset, such as a repeater or an cell base station, regions potentially suitable as a location an antenna of the asset as determined according to the prior art would typically include at least those shown in blue in the example of FIG. 4 and would typically exclude those shown in red, yellow and green in that example. FIGS. 17 and 18 are non-color counterparts of FIGS. 4 and 5 respectively.